{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting BERT Models (Part 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we demonstrate how to interpret BERT models using the `Captum` library. In this particular case study we focus on a fine-tuned Question Answering model on the SQUAD dataset using a transformers library from Hugging Face: https://huggingface.co/transformers/.\n",
    "\n",
    "We show how to use interpretation hooks to examine and better understand embeddings, sub-embeddings, BERT, and attention layers. \n",
    "\n",
    "Note: Before running this tutorial, please install `seaborn`, `pandas`, `matplotlib`, and `transformers`(from hugging face, tested on transformer version `4.3.0.dev0`) python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from transformers import BertTokenizer, BertForQuestionAnswering, BertConfig\n",
    "\n",
    "from captum.attr import visualization as viz\n",
    "from captum.attr import LayerConductance, LayerIntegratedGradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to fine-tune the BERT model on the SQUAD dataset. This can be easiy accomplished by following the steps described in hugging face's official web site: https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering.\n",
    "\n",
    "Note that the fine-tuning is done on a `bert-base-uncased` pre-trained model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we pretrain the model, we can load the tokenizer and pre-trained BERT model using the commands described below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace <PATH-TO-SAVED-MODEL> with the real path of the saved model\n",
    "model_path = '<PATH-TO-SAVED-MODEL>'\n",
    "\n",
    "# load model\n",
    "model = BertForQuestionAnswering.from_pretrained(model_path)\n",
    "model.to(device)\n",
    "model.eval()\n",
    "model.zero_grad()\n",
    "\n",
    "# load tokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A helper function to perform forward pass of the model and make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(inputs, token_type_ids=None, position_ids=None, attention_mask=None):\n",
    "    output = model(inputs, token_type_ids=token_type_ids,\n",
    "                 position_ids=position_ids, attention_mask=attention_mask, )\n",
    "    return output.start_logits, output.end_logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a custom forward function that will allow us to access the start and end postitions of our prediction using the `position` input argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def squad_pos_forward_func(inputs, token_type_ids=None, position_ids=None, attention_mask=None, position=0):\n",
    "    pred = predict(inputs,\n",
    "                   token_type_ids=token_type_ids,\n",
    "                   position_ids=position_ids,\n",
    "                   attention_mask=attention_mask)\n",
    "    pred = pred[position]\n",
    "    return pred.max(1).values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compute attributions with respect to the `BertEmbeddings` layer.\n",
    "\n",
    "To do so, we need to define baselines / references, and numericalize both the baselines and the inputs. We will define helper functions to achieve that.\n",
    "\n",
    "The cell below defines numericalized special tokens that will be later used for constructing inputs and corresponding baselines / references."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_token_id = tokenizer.pad_token_id # A token used for generating token reference\n",
    "sep_token_id = tokenizer.sep_token_id # A token used as a separator between question and text and it is also added to the end of the text.\n",
    "cls_token_id = tokenizer.cls_token_id # A token used for prepending to the concatenated question-text word sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we define a set of helper function for constructing baselines / references for word tokens, token types and position IDs. We also provide separate helper functions that allow to construct attention masks and BERT embeddings both for input and reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def construct_input_ref_pair(question, text, ref_token_id, sep_token_id, cls_token_id):\n",
    "    question_ids = tokenizer.encode(question, add_special_tokens=False)\n",
    "    text_ids = tokenizer.encode(text, add_special_tokens=False)\n",
    "\n",
    "    # construct input token ids\n",
    "    input_ids = [cls_token_id] + question_ids + [sep_token_id] + text_ids + [sep_token_id]\n",
    "\n",
    "    # construct reference token ids \n",
    "    ref_input_ids = [cls_token_id] + [ref_token_id] * len(question_ids) + [sep_token_id] + \\\n",
    "        [ref_token_id] * len(text_ids) + [sep_token_id]\n",
    "\n",
    "    return torch.tensor([input_ids], device=device), torch.tensor([ref_input_ids], device=device), len(question_ids)\n",
    "\n",
    "def construct_input_ref_token_type_pair(input_ids, sep_ind=0):\n",
    "    seq_len = input_ids.size(1)\n",
    "    token_type_ids = torch.tensor([[0 if i <= sep_ind else 1 for i in range(seq_len)]], device=device)\n",
    "    ref_token_type_ids = torch.zeros_like(token_type_ids, device=device)# * -1\n",
    "    return token_type_ids, ref_token_type_ids\n",
    "\n",
    "def construct_input_ref_pos_id_pair(input_ids):\n",
    "    seq_length = input_ids.size(1)\n",
    "    position_ids = torch.arange(seq_length, dtype=torch.long, device=device)\n",
    "    # we could potentially also use random permutation with `torch.randperm(seq_length, device=device)`\n",
    "    ref_position_ids = torch.zeros(seq_length, dtype=torch.long, device=device)\n",
    "\n",
    "    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "    ref_position_ids = ref_position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "    return position_ids, ref_position_ids\n",
    "    \n",
    "def construct_attention_mask(input_ids):\n",
    "    return torch.ones_like(input_ids)\n",
    "\n",
    "def construct_whole_bert_embeddings(input_ids, ref_input_ids, \\\n",
    "                                    token_type_ids=None, ref_token_type_ids=None, \\\n",
    "                                    position_ids=None, ref_position_ids=None):\n",
    "    input_embeddings = model.bert.embeddings(input_ids, token_type_ids=token_type_ids, position_ids=position_ids)\n",
    "    ref_input_embeddings = model.bert.embeddings(ref_input_ids, token_type_ids=ref_token_type_ids, position_ids=ref_position_ids)\n",
    "    \n",
    "    return input_embeddings, ref_input_embeddings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define the `question - text` pair that we'd like to use as an input for our BERT model and interpret what the model was focusing on when predicting an answer to the question from a given input text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "question, text = \"What is important to us?\", \"It is important to us to include, empower and support humans of all kinds.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's numericalize the question, the input text and generate corresponding baselines / references for all three sub-embeddings (word, token type and position embeddings) types using our helper functions defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_ids, ref_input_ids, sep_id = construct_input_ref_pair(question, text, ref_token_id, sep_token_id, cls_token_id)\n",
    "token_type_ids, ref_token_type_ids = construct_input_ref_token_type_pair(input_ids, sep_id)\n",
    "position_ids, ref_position_ids = construct_input_ref_pos_id_pair(input_ids)\n",
    "attention_mask = construct_attention_mask(input_ids)\n",
    "\n",
    "indices = input_ids[0].detach().tolist()\n",
    "all_tokens = tokenizer.convert_ids_to_tokens(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, let's define the ground truth for prediction's start and end positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_truth = 'to include, empower and support humans of all kinds'\n",
    "\n",
    "ground_truth_tokens = tokenizer.encode(ground_truth, add_special_tokens=False)\n",
    "ground_truth_end_ind = indices.index(ground_truth_tokens[-1])\n",
    "ground_truth_start_ind = ground_truth_end_ind - len(ground_truth_tokens) + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make predictions using input, token type, position ID and a default attention mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question:  What is important to us?\n",
      "Predicted Answer:  to include , em ##power and support humans of all kinds\n"
     ]
    }
   ],
   "source": [
    "start_scores, end_scores = predict(input_ids, \\\n",
    "                                   token_type_ids=token_type_ids, \\\n",
    "                                   position_ids=position_ids, \\\n",
    "                                   attention_mask=attention_mask)\n",
    "\n",
    "\n",
    "print('Question: ', question)\n",
    "print('Predicted Answer: ', ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two different ways of computing the attributions for emebedding layers. One option is to use `LayerIntegratedGradients` and compute the attributions with respect to `BertEmbedding`. The second option is to use `LayerIntegratedGradients` for each `word_embeddings`, `token_type_embeddings` and `position_embeddings` and compute the attributions with respect to each embedding vector.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lig = LayerIntegratedGradients(squad_pos_forward_func, model.bert.embeddings)\n",
    "\n",
    "attributions_start, delta_start = lig.attribute(inputs=input_ids,\n",
    "                                  baselines=ref_input_ids,\n",
    "                                  additional_forward_args=(token_type_ids, position_ids, attention_mask, 0),\n",
    "                                  return_convergence_delta=True)\n",
    "attributions_end, delta_end = lig.attribute(inputs=input_ids, baselines=ref_input_ids,\n",
    "                                additional_forward_args=(token_type_ids, position_ids, attention_mask, 1),\n",
    "                                return_convergence_delta=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A helper function to summarize attributions for each word token in the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summarize_attributions(attributions):\n",
    "    attributions = attributions.sum(dim=-1).squeeze(0)\n",
    "    attributions = attributions / torch.norm(attributions)\n",
    "    return attributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "attributions_start_sum = summarize_attributions(attributions_start)\n",
    "attributions_end_sum = summarize_attributions(attributions_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Visualizations For Start Position \u001b[0m\n"
     ]
    },
    {
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       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>13</b></text></td><td><text style=\"padding-right:2em\"><b>13 (0.72)</b></text></td><td><text style=\"padding-right:2em\"><b>13</b></text></td><td><text style=\"padding-right:2em\"><b>1.43</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 82%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> what                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 71%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> important                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> us                    </font></mark><mark style=\"background-color: hsl(120, 75%, 79%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ?                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(120, 75%, 88%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> important                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> us                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(0, 75%, 88%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> include                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##power                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> support                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> humans                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> all                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> kinds                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
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     "text": [
      "\u001b[1m Visualizations For End Position \u001b[0m\n"
     ]
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       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>23</b></text></td><td><text style=\"padding-right:2em\"><b>23 (0.73)</b></text></td><td><text style=\"padding-right:2em\"><b>23</b></text></td><td><text style=\"padding-right:2em\"><b>2.00</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 71%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> what                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> important                    </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> us                    </font></mark><mark style=\"background-color: hsl(120, 75%, 73%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ? 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       "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>23</b></text></td><td><text style=\"padding-right:2em\"><b>23 (0.73)</b></text></td><td><text style=\"padding-right:2em\"><b>23</b></text></td><td><text style=\"padding-right:2em\"><b>2.00</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 71%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> what                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> important                    </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> us                    </font></mark><mark style=\"background-color: hsl(120, 75%, 73%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ?                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> it                    </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> is                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> important                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 85%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> us                    </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> to                    </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> include                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ,                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> em                    </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##power                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> and                    </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> support                    </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> humans                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> of                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> all                    </font></mark><mark style=\"background-color: hsl(120, 75%, 89%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> kinds                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> .                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing couple samples in an array for visualization purposes\n",
    "start_position_vis = viz.VisualizationDataRecord(\n",
    "                        attributions_start_sum,\n",
    "                        torch.max(torch.softmax(start_scores[0], dim=0)),\n",
    "                        torch.argmax(start_scores),\n",
    "                        torch.argmax(start_scores),\n",
    "                        str(ground_truth_start_ind),\n",
    "                        attributions_start_sum.sum(),       \n",
    "                        all_tokens,\n",
    "                        delta_start)\n",
    "\n",
    "end_position_vis = viz.VisualizationDataRecord(\n",
    "                        attributions_end_sum,\n",
    "                        torch.max(torch.softmax(end_scores[0], dim=0)),\n",
    "                        torch.argmax(end_scores),\n",
    "                        torch.argmax(end_scores),\n",
    "                        str(ground_truth_end_ind),\n",
    "                        attributions_end_sum.sum(),       \n",
    "                        all_tokens,\n",
    "                        delta_end)\n",
    "\n",
    "print('\\033[1m', 'Visualizations For Start Position', '\\033[0m')\n",
    "viz.visualize_text([start_position_vis])\n",
    "\n",
    "print('\\033[1m', 'Visualizations For End Position', '\\033[0m')\n",
    "viz.visualize_text([end_position_vis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='img/bert/visuals_of_start_end_predictions.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results above we can tell that for predicting start position our model is focusing more on the question side. More specifically on the tokens `what` and `important`. It has also slight focus on the token sequence `to us` in the text side.\n",
    "\n",
    "In contrast to that, for predicting end position, our model focuses more on the text side and has relative high attribution on the last end position token `kinds`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Embedding attribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look into the sub-embeddings of `BertEmbeddings` and try to understand the contributions and roles of each of them for both start and end predicted positions.\n",
    "\n",
    "To do so, we will use `LayerIntegratedGradients` for all three layers: `word_embeddings`, `token_type_embeddings` and `position_embeddings`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create an instance of `LayerIntegratedGradients` and compute the attributions with respect to all those embeddings both for the start and end positions and summarize them for each word token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lig2 = LayerIntegratedGradients(squad_pos_forward_func, \\\n",
    "                                [model.bert.embeddings.word_embeddings, \\\n",
    "                                 model.bert.embeddings.token_type_embeddings, \\\n",
    "                                 model.bert.embeddings.position_embeddings])\n",
    "\n",
    "attributions_start = lig2.attribute(inputs=(input_ids, token_type_ids, position_ids),\n",
    "                                  baselines=(ref_input_ids, ref_token_type_ids, ref_position_ids),\n",
    "                                  additional_forward_args=(attention_mask, 0))\n",
    "attributions_end = lig2.attribute(inputs=(input_ids, token_type_ids, position_ids),\n",
    "                                  baselines=(ref_input_ids, ref_token_type_ids, ref_position_ids),\n",
    "                                  additional_forward_args=(attention_mask, 1))\n",
    "\n",
    "attributions_start_word = summarize_attributions(attributions_start[0])\n",
    "attributions_end_word = summarize_attributions(attributions_end[0])\n",
    "\n",
    "attributions_start_token_type = summarize_attributions(attributions_start[1])\n",
    "attributions_end_token_type = summarize_attributions(attributions_end[1])\n",
    "\n",
    "attributions_start_position = summarize_attributions(attributions_start[2])\n",
    "attributions_end_position = summarize_attributions(attributions_end[2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An auxiliary function that will help us to compute top-K attributions and corresponding indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topk_attributed_tokens(attrs, k=5):\n",
    "    values, indices = torch.topk(attrs, k)\n",
    "    top_tokens = [all_tokens[idx] for idx in indices]\n",
    "    return top_tokens, values, indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing interpretation hooks from all layers after finishing attribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing top-K attributions for all sub-embeddings and placing them in pandas dataframes for better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[CLS](0)',\n",
       " 'what(1)',\n",
       " 'is(2)',\n",
       " 'important(3)',\n",
       " 'to(4)',\n",
       " 'us(5)',\n",
       " '?(6)',\n",
       " '[SEP](7)',\n",
       " 'it(8)',\n",
       " 'is(9)',\n",
       " 'important(10)',\n",
       " 'to(11)',\n",
       " 'us(12)',\n",
       " 'to(13)',\n",
       " 'include(14)',\n",
       " ',(15)',\n",
       " 'em(16)',\n",
       " '##power(17)',\n",
       " 'and(18)',\n",
       " 'support(19)',\n",
       " 'humans(20)',\n",
       " 'of(21)',\n",
       " 'all(22)',\n",
       " 'kinds(23)',\n",
       " '.(24)',\n",
       " '[SEP](25)']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_words_start, top_words_val_start, top_word_ind_start = get_topk_attributed_tokens(attributions_start_word)\n",
    "top_words_end, top_words_val_end, top_words_ind_end = get_topk_attributed_tokens(attributions_end_word)\n",
    "\n",
    "top_token_type_start, top_token_type_val_start, top_token_type_ind_start = get_topk_attributed_tokens(attributions_start_token_type)\n",
    "top_token_type_end, top_token_type_val_end, top_token_type_ind_end = get_topk_attributed_tokens(attributions_end_token_type)\n",
    "\n",
    "top_pos_start, top_pos_val_start, pos_ind_start = get_topk_attributed_tokens(attributions_start_position)\n",
    "top_pos_end, top_pos_val_end, pos_ind_end = get_topk_attributed_tokens(attributions_end_position)\n",
    "\n",
    "df_start = pd.DataFrame({'Word(Index), Attribution': [\"{} ({}), {}\".format(word, pos, round(val.item(),2)) for word, pos, val in zip(top_words_start, top_word_ind_start, top_words_val_start)],\n",
    "                   'Token Type(Index), Attribution': [\"{} ({}), {}\".format(ttype, pos, round(val.item(),2)) for ttype, pos, val in zip(top_token_type_start, top_token_type_ind_start, top_words_val_start)],\n",
    "                   'Position(Index), Attribution': [\"{} ({}), {}\".format(position, pos, round(val.item(),2)) for position, pos, val in zip(top_pos_start, pos_ind_start, top_pos_val_start)]})\n",
    "df_start.style.apply(['cell_ids: False'])\n",
    "\n",
    "df_end = pd.DataFrame({'Word(Index), Attribution': [\"{} ({}), {}\".format(word, pos, round(val.item(),2)) for word, pos, val in zip(top_words_end, top_words_ind_end, top_words_val_end)],\n",
    "                   'Token Type(Index), Attribution': [\"{} ({}), {}\".format(ttype, pos, round(val.item(),2)) for ttype, pos, val in zip(top_token_type_end, top_token_type_ind_end, top_words_val_end)],\n",
    "                   'Position(Index), Attribution': [\"{} ({}), {}\".format(position, pos, round(val.item(),2)) for position, pos, val in zip(top_pos_end, pos_ind_end, top_pos_val_end)]})\n",
    "df_end.style.apply(['cell_ids: False'])\n",
    "\n",
    "['{}({})'.format(token, str(i)) for i, token in enumerate(all_tokens)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we can see the top 5 attribution results from all three embedding types in predicting start positions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 5 attributed embeddings for start position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word(Index), Attribution</th>\n",
       "      <th>Token Type(Index), Attribution</th>\n",
       "      <th>Position(Index), Attribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>it (8), 0.41</td>\n",
       "      <td>, (15), 0.41</td>\n",
       "      <td>##power (17), 0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>, (15), 0.4</td>\n",
       "      <td>of (21), 0.4</td>\n",
       "      <td>it (8), 0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>important (3), 0.35</td>\n",
       "      <td>include (14), 0.35</td>\n",
       "      <td>humans (20), 0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>to (13), 0.29</td>\n",
       "      <td>em (16), 0.29</td>\n",
       "      <td>us (12), 0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>us (5), 0.23</td>\n",
       "      <td>and (18), 0.23</td>\n",
       "      <td>to (13), 0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Word(Index), Attribution Token Type(Index), Attribution  \\\n",
       "0             it (8), 0.41                   , (15), 0.41   \n",
       "1              , (15), 0.4                   of (21), 0.4   \n",
       "2      important (3), 0.35             include (14), 0.35   \n",
       "3            to (13), 0.29                  em (16), 0.29   \n",
       "4             us (5), 0.23                 and (18), 0.23   \n",
       "\n",
       "  Position(Index), Attribution  \n",
       "0            ##power (17), 0.5  \n",
       "1                 it (8), 0.32  \n",
       "2            humans (20), 0.26  \n",
       "3                us (12), 0.26  \n",
       "4                to (13), 0.22  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Word embeddings help to focus more on the surrounding tokens of the predicted answer's start position to such as em, ##power and ,. It also has high attribution for the tokens in the question such as what and ?.\n",
    "\n",
    "In contrast to word embedding, token embedding type focuses more on the tokens in the text part such as important,em and start token to.\n",
    "\n",
    "Position embedding also has high attribution score for the tokens surrounding to such as us and important. In addition to that, similar to word embedding we observe important tokens from the question.\n",
    "\n",
    "We can perform a similar analysis, and visualize top 5 attributed tokens for all three embedding types, also for the end position prediction.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 5 attributed embeddings for end position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word(Index), Attribution</th>\n",
       "      <th>Token Type(Index), Attribution</th>\n",
       "      <th>Position(Index), Attribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kinds (23), 0.49</td>\n",
       "      <td>and (18), 0.49</td>\n",
       "      <td>kinds (23), 0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>, (15), 0.34</td>\n",
       "      <td>[SEP] (25), 0.34</td>\n",
       "      <td>humans (20), 0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>it (8), 0.33</td>\n",
       "      <td>[SEP] (7), 0.33</td>\n",
       "      <td>. (24), 0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>of (21), 0.29</td>\n",
       "      <td>of (21), 0.29</td>\n",
       "      <td>to (4), 0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>? (6), 0.25</td>\n",
       "      <td>, (15), 0.25</td>\n",
       "      <td>of (21), 0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Word(Index), Attribution Token Type(Index), Attribution  \\\n",
       "0         kinds (23), 0.49                 and (18), 0.49   \n",
       "1             , (15), 0.34               [SEP] (25), 0.34   \n",
       "2             it (8), 0.33                [SEP] (7), 0.33   \n",
       "3            of (21), 0.29                  of (21), 0.29   \n",
       "4              ? (6), 0.25                   , (15), 0.25   \n",
       "\n",
       "  Position(Index), Attribution  \n",
       "0             kinds (23), 0.85  \n",
       "1            humans (20), 0.23  \n",
       "2                 . (24), 0.17  \n",
       "3                 to (4), 0.17  \n",
       "4                of (21), 0.14  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to observe a high concentration of highly attributed tokens such as `of`, `kinds`, `support` and `##power` for end position prediction.\n",
    "\n",
    "The token `kinds`, which is the correct predicted token appears to have high attribution score both according word and position embeddings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting BERT Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look into the layers of our network. More specifically we would like to look into the distribution of attribution scores for each token across all layers of the BERT model and dive deeper into specific tokens.\n",
    "We do that using one of layer attribution algorithms, namely, layer conductance. However, we encourage you to try out and compare the results with other algorithms as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define another version of squad forward function that takes emebddings as an input argument. This is necessary for `LayerConductance` algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def squad_pos_forward_func2(input_emb, attention_mask=None, position=0):\n",
    "    pred = model(inputs_embeds=input_emb, attention_mask=attention_mask, )\n",
    "    pred = pred[position]\n",
    "    return pred.max(1).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's iterate over all layers and compute the attributions for all tokens. In addition to that let's also choose a specific token that we would like to examine in detail, specified by an ID `token_to_explain` and store related information in a separate array.\n",
    "\n",
    "\n",
    "Note: Since the below code is iterating over all layers it can take over 5 seconds. Please be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_attrs_start = []\n",
    "layer_attrs_end = []\n",
    "\n",
    "# The token that we would like to examine separately.\n",
    "token_to_explain = 23 # the index of the token that we would like to examine more thoroughly\n",
    "layer_attrs_start_dist = []\n",
    "layer_attrs_end_dist = []\n",
    "\n",
    "input_embeddings, ref_input_embeddings = construct_whole_bert_embeddings(input_ids, ref_input_ids, \\\n",
    "                                         token_type_ids=token_type_ids, ref_token_type_ids=ref_token_type_ids, \\\n",
    "                                         position_ids=position_ids, ref_position_ids=ref_position_ids)\n",
    "\n",
    "for i in range(model.config.num_hidden_layers):\n",
    "    lc = LayerConductance(squad_pos_forward_func2, model.bert.encoder.layer[i])\n",
    "    layer_attributions_start = lc.attribute(inputs=input_embeddings, baselines=ref_input_embeddings, additional_forward_args=(attention_mask, 0))\n",
    "    layer_attributions_end = lc.attribute(inputs=input_embeddings, baselines=ref_input_embeddings, additional_forward_args=(attention_mask, 1))\n",
    "    layer_attrs_start.append(summarize_attributions(layer_attributions_start).cpu().detach().tolist())\n",
    "    layer_attrs_end.append(summarize_attributions(layer_attributions_end).cpu().detach().tolist())\n",
    "\n",
    "    # storing attributions of the token id that we would like to examine in more detail in token_to_explain\n",
    "    layer_attrs_start_dist.append(layer_attributions_start[0,token_to_explain,:].cpu().detach().tolist())\n",
    "    layer_attrs_end_dist.append(layer_attributions_end[0,token_to_explain,:].cpu().detach().tolist())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below represents a heat map of attributions across all layers and tokens for the start position prediction. \n",
    "It is interesting to observe that the question word `what` gains increasingly high attribution from layer one to nine. In the last three layers that importance is slowly diminishing.  \n",
    "In contrast to `what` token, many other tokens have negative or close to zero attribution in the first 6 layers. \n",
    "\n",
    "We start seeing slightly higher attribution in tokens `important`, `us` and `to`. Interestingly token `em` is also assigned high attribution score which is remarkably high in the last three layers.\n",
    "And lastly, our correctly predicted token `to` for the start position gains increasingly positive attribution has relatively high attribution especially in the last two layers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "xticklabels=all_tokens\n",
    "yticklabels=list(range(1,13))\n",
    "ax = sns.heatmap(np.array(layer_attrs_start), xticklabels=xticklabels, yticklabels=yticklabels, linewidth=0.2)\n",
    "plt.xlabel('Tokens')\n",
    "plt.ylabel('Layers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's examine the heat map of the attributions for the end position prediction. In the case of end position prediction we again observe high attribution scores for the token `what` in the last 11 layers.\n",
    "The correctly predicted end token `kinds` has positive attribution across all layers and it is especially prominent in the last two layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "\n",
    "xticklabels=all_tokens\n",
    "yticklabels=list(range(1,13))\n",
    "ax = sns.heatmap(np.array(layer_attrs_end), xticklabels=xticklabels, yticklabels=yticklabels, linewidth=0.2) #, annot=True\n",
    "plt.xlabel('Tokens')\n",
    "plt.ylabel('Layers')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to note that when we compare the heat maps of start and end position, overall the colors for start position prediction on the map have darker intensities. This implies that there are less tokens that attribute positively to the start position prediction and there are more tokens which are negative indicators or signals of start position prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's dig deeper into specific tokens and look into the distribution of attributions per layer for the token `kinds` in the start and end positions. The box plot diagram below shows the presence of outliers especially in the first four layers and in layer 8. We also observe that for start position prediction interquartile range slowly decreases as we go deeper into the layers and finally it is dimishing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "ax = sns.boxplot(data=layer_attrs_start_dist)\n",
    "plt.xlabel('Layers')\n",
    "plt.ylabel('Attribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot same distribution but for the prediction of the end position. Here attribution has larger positive values across all layers and the interquartile range doesn't change much when moving deeper into the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "ax = sns.boxplot(data=layer_attrs_end_dist)\n",
    "plt.xlabel('Layers')\n",
    "plt.ylabel('Attribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to that we can also look into the distribution of attributions in each layer for any input token. This will help us to better understand and compare the distributional patterns of attributions across multiple layers. We can for example represent attributions as a probability density function (pdf) and compute the entropy of it in order to estimate the entropy of attributions in each layer. This can be easily computed using a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pdf_attr(attrs, bins=100):\n",
    "    return np.histogram(attrs, bins=bins, density=True)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this particular case let's compute the pdf for the attributions at end positions `kinds`. We can however do it for all tokens.\n",
    "\n",
    "We will compute and visualize the pdfs and entropies using Shannon's Entropy measure for each layer for token `kinds`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_attrs_end_pdf = map(lambda layer_attrs_end_dist: pdf_attr(layer_attrs_end_dist), layer_attrs_end_dist)\n",
    "layer_attrs_end_pdf = np.array(list(layer_attrs_end_pdf))\n",
    "\n",
    "# summing attribution along embedding diemension for each layer\n",
    "# size: #layers\n",
    "attr_sum = np.array(layer_attrs_end_dist).sum(-1)\n",
    "\n",
    "# size: #layers\n",
    "layer_attrs_end_pdf_norm = np.linalg.norm(layer_attrs_end_pdf, axis=-1, ord=1)\n",
    "\n",
    "#size: #bins x #layers\n",
    "layer_attrs_end_pdf = np.transpose(layer_attrs_end_pdf)\n",
    "\n",
    "#size: #bins x #layers\n",
    "layer_attrs_end_pdf = np.divide(layer_attrs_end_pdf, layer_attrs_end_pdf_norm, where=layer_attrs_end_pdf_norm!=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot below visualizes the probability mass function (pmf) of attributions for each layer for the end position token `kinds`. From the plot we can observe that the distributions are approximately bell-curved shapes with different means and variances.\n",
    "We can now use attribution pdfs to compute entropies in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plt.plot(layer_attrs_end_pdf)\n",
    "plt.xlabel('Bins')\n",
    "plt.ylabel('Density')\n",
    "plt.legend(['Layer '+ str(i) for i in range(1,13)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we calculate and visualize attribution entropies based on the Shannon entropy measure where the x-axis corresponds to the number of layers and the y-axis corresponds to the total attribution in that layer. The size of the circles for each (layer, total_attribution) pair correspond to the normalized entropy value at that point.\n",
    "\n",
    "In this particular example, we observe that the entropy doesn't change much from layer to layer, however in a general case entropy can provide an intuition about the distributional characteristics of attributions in each layer and can be useful especially when comparing it across multiple tokens.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7H9zX4aqwa049av+MTzRztOzog+amfw92RgLA7rDnFQB2oNUqedOzl+bcUw7LRVfcm89+754Mj02kp1UyMjaemqSvp5VSkrHxmpeeekTedvYxOeGw/Wa69VnhirseTV9PSadHFA2NTuRrNz2UXznnuM4Whl2w9OB5WXzA3Ny+akNH687p68kbn3l0R2sCwFQIjwBgEocsGMx/+tnj8+4XPTn3PbY5N9y/Ng+vG8pErdl/bn9OOmK/PPmQ+entsRNgV1x775psGmnmVua3PrQ+tdaUYt4UM+cdzzsuv/+lGzv677ym5hVnHNWxetCEWmtuemBdrl+5Njc/uDYbh8ez32BvTjpiYU5dvH+OP3S+788wCwmPAGAKSilZctDcLDnIcbROuO3h9WnqhlEjYxPZODKe+QN+zGHmvOzpR+aTl96V21etTydOsM3p68lv/tzx2W+wb8+LQQOGx8Zz0RX35pOX3Zn1Q2OptWbzNnPt5vb3pNZk0YKBvON5x+U/LFvsphIwi/g1KQAw7cYavJV5q5RG68NU9LRKPvr60zPQu+eDs/taJccdMi9vO+uYDnQGnXfj/Wvzoj+9NB/8+i15eN1wNo2M/0RwlGTrc+O597FN+YN/vjnnffjy3Lm6s0c7geYIjwCAabffnOZ2T4zXmjm7cac86LQnHTI/H339aRns2/0fuft6Sg5dOJjP/dKZadmlQRf61oqH86pPfC/3Pb75pwKjdjaPjuf2Vevzi3/5nSy/57GGOwQ6QXgEAEy7M44+IH09zbwQXrRgoCO7PaATXnDCofnUm5+R/QZ707+L/+bn9vXkpCMW5iu//pwcOK+/oQ5h9139o8fzzr+7JkNTDI22NVG37EZ606evyh2r1jfQHdBJwiMAYNqdunj/xgKe0xfv30hd2F1nPengXPpbz88LTzw0A72tnQan8/p7Mre/J7/7khNy8TueLTiiK20eGc87/vbq3QqOfqLO6Hje8bfXOG4MXc4kSQBg2i07+oD0NrDzaF5/T1575pKO14U9dcC8/nz8DWdk5eOb8rnv/yj/dtNDWfn45vT3tlKSjE3UtErJCYctyOvOXJJfPPWIDPbZQUf3+ui378i6zaN7XKfWZOWazfn8Vffmjc9auueNAY0QHgEA0663p5W3PvuYfPySOzI01rnfNs8f7M1Zxx3csXrQaUcdMDfvO+/EvO+8EzMyNpEH127O6HjN/IHeHLrfgFuYMyuMjE3ks9+/p2PfvzePjOfjl96ZNzzzaF8D0KUcWwMAZsTbzz4m8wc793usOX09+aNXnGKoMLNGf28rRx80L086ZH4OWzjoRTOzxnfveCS11o7WXLNpNDfev66jNYHOER4BADNi3kBv/uK1e3YnqicM9LbywhMPyQtOOLQDnQEwmWvufTybR8Y7WnOi1ly3ck1HawKdIzwCAGbMs487OL9/3ol7FCAN9rby1MP3y/989akd7AyAdq69d03GO7vxKEOjE7n+PuERdCvhEQAwo97wrKX5wPmnZE5fT3p28cjZnL6enH38onz+gmcaLgwwTTYMjzVSd93Qng/gBpphYDYAMONecfpRecbSA/ObX7guN9y/NqNjE5P+VnveQE96W6388StOyUtOOXz6GgUgg73N7EGY45cA0LWERwBAV1h84Nz8w68+O7c8tC6f+c49ufT21Xlk/XAGeltJScbGa3pbJScfuTBvfvbS/OxTD01fj03UANPt5CMX5sp7HksnZ2b397Zy8pELO1cQ6CjhEQDQVU44bL/8j1c9LUmycXgsD60bysREzX5z+nLIArcyB5hppy05IPN+cF9Hj6/195ScIjyCriU8AgC61ryB3hy3aP5MtwHANp73lEUZm5joaM3eVitnHH1AR2sCnWOvNwAAAFM2b6A3L3/6kenr6cxO0IHeVt707KPT6ygydC1fnQAAAOyS9774KRno7cyA6wWDvfmV5x7XkVpAM4RHAAAA7JKD5w/kg696Wub07dlLysG+Vv7ytadn3oCJKtDNhEcAAADsspeccnj+088en8HdDJAG+1r5wPmn5FnHHdThzoBOE+8CAACwWy547nE5fOGc/O7F12dkdCKjE3WnH9Pf28qcvp78xWtPyznHL5qGLoE9JTwCAABgt/3iqUfkzGMPzB9/9Zb8yw0PprdVsnFk/CfWlCRz+3syUZNXnnFkfuvnTsjCuX0z0zCwy0qtO0+Gu8myZcvq8uXLZ7oNAAAAtrN282j+/ZaHs/yex3PD/WszPDqRuQM9OfWo/XPG0QfkhScekrn99jBANyqlXF1rXbaja75qAQAA6IiFc/py/mlH5fzTjprpVoAOMjAbAAAAgLaERwAAAAC0JTwCAAAAoC3hEQAAAABtCY8AAAAAaEt4BAAAAEBbwiMAAAAA2hIeAQAAANCW8AgAAACAtoRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0JbwCAAAAIC2hEcAAAAAtCU8AgAAAKCt3pluAAAAANh9tdY8smEkw2Pj6e9tZdH8gZRSZrot9iLCIwAAAJhl1g+N5p+uuT9fvPb+3PbQ+kzUmp5WyfhETSnJkw9ZkJc//Yi8atniLJzTN9PtMssJjwAAAGCWGB2fyF9+6/ZcePldaZWSTSPjO1x3w/1rc8eqDfmTr9+aNz9rad7z4uMz0Nszzd2ytxAeAQAAwCxwzyMb85bPXJWH1w1naHRip+s3j24Jlj53xT356o0P5jNveUaefOiCpttkL2RgNgAAAHS5O1ZtyMs++p3c+9imH4dCUzU0OpH7H9+c8z/2vax4cF1DHbI3Ex4BAABAF1s3NJrXXPj9rBsay0TdvRo1yYbhsbz2wivy+MaRjvbH3q+x8KiUsriU8u1SyopSyk2llHftYM3rSynXb318r5RyalP9AAAAwGz0X750YzYMjaXuZnC0rY0jY/mdL16/54XYpzS582gsyXtqrScmeWaSd5ZSnrrdmruTnFNrfVqS9ye5sMF+AAAAYFa58f61+dpND2V4bOczjqZidLzm8tsfyVV3P9aReuwbGguPaq0P1lqv2fr2+iQrkhy53Zrv1Vof3/ruFUmOaqofAAAAmG0uvOyujHQoOHrC0Oh4PnnZnR2tyd5tWmYelVKWJjktyZWTLHtbkn+djn4AAACg2w2PjedrNz6023OO2qlJLr/tkWwYHutsYfZajYdHpZT5Sb6Y5N211h2OdS+lPD9bwqPfaXP9glLK8lLK8tWrVzfXLAAAAHSJ2x7akP7eZl62D/S2cvMD7rzG1DQaHpVS+rIlOLqo1npxmzVPS/JXSV5Wa310R2tqrRfWWpfVWpctWrSouYYBAACgS9z0wNqMd3rb0VYj4xO58f61jdRm79Pk3dZKkk8lWVFr/VCbNUuSXJzkjbXW25rqBQAAAGabtZtHMzre2XlHTxgem8iaTSON1Gbv09tg7bOSvDHJDaWU67Y+974kS5Kk1vqJJP8lyUFJPrYla8pYrXVZgz0BAADArNAqJVteKjejpzUtY5DZCzQWHtVav5Nk0n/mtda3J3l7Uz0AAADAbHXowsEM9LYyOj7e8dpz+npy+MLBjtdl7yRmBAAAgC50ypELM97MyKO0WsnJRy5spjh7HeERAAAAdKGjD5ybvp5mzq3Vmjz50PmN1GbvIzwCAACALtRqlbzpmUsz0NvZl+59PSWvecbi9PWIBJga/1IAAACgS73xWUen1eGp2T2tkl8665iO1mTvJjwCAACALnXofoP5rRc/JXP6ezpSb25/T975/Cdl8YFzO1KPfYPwCAAAALrYW569NE87cuEeH1/r723lSYfMzzvOOa5DnbGvEB4BAABAF2u1Sj7z1mfkxMMXZHA3A6SB3laOWzQ/f/v2M9Nr1hG7yL8YAAAA6HJz+3vzvy94Vl7zjMUZ7Nu1l/KDfa2cf9qR+eI7npX9Bvsa6pC9We9MNwAAAADs3GBfT/7by07OS59+ZD7w1RW58f61qTUZGZ/4qbV9PSU9peT4wxbkfeedmGcee9AMdMzeQngEAAAAs8gZRx+QL77j2bn7kY355s0P54q7H82tD63PyNhE+ntbefIh83PmsQflRScekicdsmCm22UvUGqtM93DLlm2bFldvnz5TLcBALDXmZioWbN5NGMTE5k/0Ju5/X7PCAD7ilLK1bXWZTu65icCAIB92MPrhvK3V/wo31qxKneu3pCapCQZm6g5cG5/Tl28MK87c0nOOf6Q9LTKTLcLAMwA4REAwD7o8Y0j+f0v3Zhvrng4STI89tPzMlZvGM43V6zK9+98NIN9PfnD80/OuScfPt2tAgAzzN3WAAD2MZfetjrnfPDb+cbND2V4bGKHwdG2No6M59GNI/lPf//D/PLnlmfTyNg0dQoAdAPhEQDAPuSrNzyQX/mb5Vk3NJaR8V2bfbl5dDyX3bY6r/z497JxWIAEAPsKx9YAZrE1m0Zy0wPrsnr9cGpqDpjbn5OOWJhFCwZmujWgC11335r85hd+mKHRyXcaTWZ4bCJ3rd6YX/7c8lz09jNTijlIALC3Ex4BzDJrNo3kC8vvy2e+e08e2TCcwd6eTKQmNWm1SoZHJzJ/sDdveOaSvOHMo3PIfoMz3TLQBYZGx/NrF129R8HRE4bHJnLtvWvyj1evzKuXLe5AdwBANxMeAcwStdb87RU/yge+ekuSms1bXwCOjv/00ZHHNo7kk5felU9eelfecc5x+fUXPCm9PU4qw77s09+9O49tHOlYvc2j4/mvX7kpP/+0wzO334+UALA380oCYBbYMDyW13zy+/mjf70lm0fHfxwcTeaJIbifvOzOvOTDl2f1+uFp6BToRuMTNX91+d0d2XW0va9c90DHawIA3UV4BNDlNg6P5VUf/16uW7k2m0bGd/njN49O5O5HNualH/lOHtkgQIJ90ZV3PZqRsV3//rEzm0bG8+nv3t3xugBAdxEeAXS59/7DD3P3IxszspNbaU9mbKJm9frhvO2zP8jExK7dXQmY/a6+9/FGdh0lyd2PbMzoeDO1AYDuIDwC6GLfuPnhXHLrqgzvQXD0hLGJmtsf3pDPff+ePa4FzC4/uPuxjDUUHPf3tnL7wxsaqQ0AdAfhEUCXmpio+f0v3TCl+UZTtWlkPH/y9VszNNr54ytA93q0g4Oyt9cqJWs2NVcfAJh5wiOALvW9Ox/N+qGfvpNaJ/zzDw24hX1Jabp+afozAAAzSXgE0KX+fvm9uzUge2c2jYzn7668t+N1ge51+P5zGqs9PlFzyH4DjdUHAGae8AigS11775rGat/y0PrUanA27CvOPObA9Pc282Pf+ETNMQfNa6Q2ANAdhEcAXWhsfCIPrhlqrH5NzUPrmqsPdJefOebA9LaaOVp28pEL02qoNgDQHYRHAF1o8+h4Wg1+h+5ttRqbpwR0n1OOXJhF8zt/tGzeQE9++exjO14XAOguwiOALtTbamWiczdZ+ym11vTYKQD7jFJKfuMFT8rc/p6O1p3T15MXnXhIR2sCAN1HeATQhQb7Whnsa+5b9PDYRI5Y2NwAXaD7vOL0o/LkQ+enp0N3Rhvsa+XD//G09Pb4cRIA9nb+twfoQqWUPOWwBY3VP2S/gczp8A4EoLu1WiUfee3pHfnan9PXk1ecflTOetLBHegMAOh2wiOALvWiEw9tZPdRTys553jHTGBftPjAufm7Xz4z8wd6s7sbkOb09eR5JyzK+192cmebAwC6lvAIoEv9h2csTq2dr9vX08pbz1ra+cLArPC0o/bPl955Vp60aH7m9E19F1IpW46q/co5x+ajrz3d3DQA2IcIjwC61MHzB3LuyYelv4PzRHpaJaccuTDHH9rckTig+z3pkPn513ednXe96MlZOKcv8wbah0h9PSUDva38zNID8+V3PifvftHxaQmOAGCf0jvTDQDQ3h+89ORccuvqjGzuzK3X+nta+dB/eHpHagGzW29PK796znF5+3OOybduWZVv37IqV//o8Ty0dijjtWZuf09OOGy/POu4g/LSU4/I4gPnznTLAMAMER4BdLGFc/vyl689LRf8zfIMje5ZgDTY18ofvOwkLwCBn9Db08qLTzosLz7psIEGWoEAACAASURBVJluBQDoUo6tAXS55x6/KP/z1afu0fDswb5W3vtzT8mrly3uYGcAAMC+wM4jgFngF552RA7dbzDvvOiarNs8mqGxqe1C6u9pZbC/lQ+9+ul50VMPbbhLAABgb2TnEcAs8YylB+bS33p+fuk5x2T+QG/mD/Sk3cjaef09mdPXk1cvOyqX/dbzBUcAAMBuK7WJ+0A3aNmyZXX58uUz3QbAjBoeG8+/r1iV79/1aH5wz2N5dMNIkmThnL6cfvQBeeaxB+bFJx2Wuf02mAIAADtXSrm61rpsR9e8qgCYhQZ6e/KSUw7PS045fKZbAQAA9nKOrQEAAADQlvAIAAAAgLaERwAAAAC0JTwCAAAAoC3hEQAAAABtCY8AAAAAaEt4BAAAAEBbwiMAAAAA2hIeAQAAANCW8AgAAACAtoRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0JbwCAAAAIC2hEcAAAAAtCU8AgAAAKCtxsKjUsriUsq3SykrSik3lVLetYM1pZTyF6WUO0op15dSTm+qHwAAAAB2XW+DtceSvKfWek0pZUGSq0sp36i13rzNmpckefLWx5lJPr71TwAAAAC6QGM7j2qtD9Zar9n69vokK5Icud2ylyX5XN3iiiT7l1IOb6onAAAAAHbNtMw8KqUsTXJakiu3u3Rkkvu2eX9lfjpgAgAAAGCGNB4elVLmJ/liknfXWtdtf3kHH1J3UOOCUsryUsry1atXN9EmAAAAADvQaHhUSunLluDoolrrxTtYsjLJ4m3ePyrJA9svqrVeWGtdVmtdtmjRomaaBQAAAOCnNHm3tZLkU0lW1Fo/1GbZV5K8aetd156ZZG2t9cGmegIAAABg1zR5t7WzkrwxyQ2llOu2Pve+JEuSpNb6iSRfTXJekjuSbEry1gb7AQAAAGAXNRYe1Vq/kx3PNNp2TU3yzqZ6AAAAAGDPTMvd1gAAAACYnYRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0FbvzhaUUvqTvDzJ0m3X11o/0FxbAAAAAHSDnYZHSf4pyVCSq5OMN9sOAAAAAN1kKuHR0bXWkxvvBAAAAICuM5WZR1eUUp7aeCcAAAAAdJ2p7Dw6M8m1pZQ7kgwnKUlqrfX0RjsDAAAAYMZNJTx6eeNdAAAAANCVdnpsrdZ6Z5I5SX5262Nw63MAAAAA7OV2Gh6VUn49yReSLNn6+EIp5deabgwAAACAmTeVY2sXJPmZWuuGJCmlfCDJ95J8rMnGAAAAAJh5U7nbWkkyus37o1ufAwAAAGAvN5WdR3+T5IpSyhe3vn9+ks821xIAAAAA3WKn4VGt9U9KKd9Ocna27Dj61VrrDxrvDAAAAIAZ1zY8KqXMq7VuLKXsl+TWrY8nru1Xa103HQ0CAAAAMHMm23n0j0lekuSmJHWb58vW95c02BcAAAAAXaBteFRrfcnWPxdPXzsAAAAAdJOd3m2tlPJvU3kOAAAAgL3PZDOP+pMMJjm0lLIgW46rJcl+cWQNAAAAYJ8w2cyjdyb5zSSHJLl5m+fXJflEk00BAAAA0B0mm3n0Z0n+rJTy7lrrn09jTwAAAAB0icl2Hj1hVSnldds/WWv9uwb6AQAAAKCLTCU8OnubtweTvCDJ1UmERwAAAAB7uZ2GR7XWd2z7finlgCR/3VRDwOz08Lqh/MPy+3LZbatzy0Prs354LCXJgsG+nHDYgjz3+IPz6mWLc8iCwZluFQAAgF0wlZ1H21uf5PhONwLMTg+vG8rvf+nGXHrb6iTJyNjEj6/VJGs3j+bKux/LdfetyYe/dUee/5RFef/LTxYiAQAAzBI7DY9KKf+ULa8Bk6SV5KQkX2qyKWB2+PK19+f3/umGjIxNZGyiTrp2eGuo9K0Vq/KdOy7J/3jl0/ILTztiOtoEAABgD0xl59FHtnl7LMmPaq33NNMOMFtceNmd+dA3bsvQ6MTOF29jbKJmbHg87/2HH+aR9cN5y1nHNNQhAAAAndDa2YJa67eSXJ9kIMncJBubbgrobv/n+gd2Kzja1tDoRP74a7fm6zc91MHOAAAA6LSdhkellLcmuSbJ65K8IcnyUsqbm24M6E6r1w/nd794wx4FR08YGt2yA+mxjSMd6AwAAIAm7DQ8SvK7SU6vtb6h1vr6JGckeV+zbQHd6g/++aYMj453rN7Q6Hje/39u7lg9AAAAOmsq4dH9SdZs8/7aJCubaQfoZo9tHMm/3fxwRncyHHtXjI7XfPWGB7N282jHagIAANA5bQdml1L+n61v3pvk+6WUL2XLXddenuQH09Ab0GW+ct39KaXzdVul5P9c/0Bef+bRnS8OAADAHpls59GirY/7kvxrtgzMHkzytSSPNt8a0G0uv+ORjsw62t7m0fF89/ZHOl4XAACAPdd251Gt9f+dzkaA7nfj/Wsbq33dyjU7XwQAAMC0m+zY2p/WWt9TSvmnbDmu9hNqra9otDOg66wfGpuVtQEAANh9bcOjJH+/9c+PTEcjwL6tgVFKAAAAdMBkx9auKqX0JHlTrfXN09gT0KUOmNufTSObG6l94Lz+RuoCAACwZyYbmJ1a63iSw0spfdPUD9DFnr54YWO1T1tyQGO1AQAA2H2THVt7wl1JLi+lfDnJxieerLX+RWNdAV3pnOMPybdvXZ1NI+MdrTu3vyfPPf7gjtYEAACgMybdebTV6iTfSDI3yaKtD6/yYB/08087PPWnxud3xktOPryZwgAAAOyRqew8urbWevG2T5RS3GkN9kHzBnrz6mVH5e9/cF+GxyY6UnOwt5XXnbkkg309HakHAABAZ01l59Hv7+C5/9zpRoDZ4XfOPSELBqeSO0/NfnP68p6ffUrH6gEAANBZbV8BllJenOTcJEeWUj60zaX9knRmywEw68wb6M1HX3d63vyZqzI0umffCgb7Wvn4G07PnH67jgAAALrVZDuPViW5MclQkpu2efxbkpc03xrQrc489qB8+DVPz2DfVDYv7thgXysffd3pOePoAzvYGQAAAJ3WdudRrfXaJNeWUubVWj+17bVSyq8n+UjTzQHd68UnH56LFgzk1y66Jms3jWZoijOQBntbOWBefz72+tNz2pIDGu4SAACAPTWVbQNv2cFzb+twH8AsdMbRB+aS9z4/v/ScYzJ/oDfzB3pSdrCuJJk30JMFg715+9nH5tvvfZ7gCAAAYJaYbObRa5L8xyTHlFK2vdvafknWNN0YMDvM6e/Jb597Qt71oifnWytW5Yq7Hs3yex7PoxuHU1Jy0Pz+LDv6gDzruIPyghMOTX/v7h91AwAAYPpNdsukq5I8muSoJB/d5vn1Sa5psilg9hno7cl5pxye8045fKZbAQAAoIMmm3l0d5K7k3xz2+dLKWcm+dMk72q2NQAAAABm2mQ7j36slHJyktcleU2SB5JcPPlHAAAAALA3mGzm0bHZMvPodUk2JPn7JH211rOnqTcAAAAAZthkO4/uSHJ5klfUWm9LklLKb0xLVwAAAAB0hclue/SaJI8k+VYp5WOllHOSHd6FGwAAAIC9VNvwqNb6D7XWVyZ5apIrk/xeksNKKX9ZSnnBdDUIAAAAwMyZbOdRkqTWur7W+tla67lJFie5Jcn/13RjAAAAAMy8nYZH26q1PlJr/Wit9bk7W1tK+XQpZVUp5cY21xeWUv65lPLDUspNpZS37kovAAAAADRvl8KjXfTXSc6d5Po7k9xcaz01yfOS/Gkppb/BfgAAAADYRY2FR7XWy5I8NtmSJAtKKSXJ/K1rx5rqBwAAAIBd1zuDn/sjSb6S5IEkC5K8ptY6MYP9AAAAALCdtuFRKeXxbNkd9FOXktRa64F7+LlfnOS6JC9IclySb5RSLq+1rttBLxckuSBJlixZsoefFgAAAICpmuzY2sFJFu3g8cTze+qtSS6uW9yR5O4kJ+xoYa31wlrrslrrskWLOvGpAQAAAJiKtuFRrXV820eShUkO3eaxp+5N8sIkKaUcmuQpSe7qQF0AAAAAOmSnM49KKT+f5M+SHJXk0SRHJrktbXYJbfNxn8+Wu6gdXEpZmeS/JulLklrrJ5K8P8lfl1JuyJajcL9Ta31kt/8mAAAAAHTcVAZm/2GSs5L8W631tFLKzyZ55c4+qNb62p1cfyDJz02pSwAAAABmxGQzj54wVmtdnaRVSim11m8kOb3hvgAAAADoAlPZebS2lDIvyXeSfK6UsirJRLNtAQAAANANprLz6OVJhpK8O8klSe5P8gsN9gQAAABAl5hKePR7W++4Nlpr/VSt9UNJfrPpxgAAAACYeVMJj87dwXM/3+lGAAAAAOg+bWcelVJ+JcmvJjm+lHLNNpcWJFnedGMAAAAAzLzJBmZ/Icm3kvxRkt/d5vn1tdZVjXYFAAAAQFdoGx7VWh9P8niSV5dSTk7ynK2XLk8iPAIAAADYB+x05lEp5Z3ZsgtpydbHF0opv9Z0YwAAAADMvMmOrT3hV5L8TK11Q5KUUj6Q5HtJPtZkYwAAAADMvKncba0kGd3m/dGtzwEAAACwl5vsbmu9tdaxJH+T5IpSyhe3Xjo/yWenozkAAAAAZtZkx9auSnJ6rfVPSinfTnJ2tuw4+tVa6w+mpTsAAAAAZtRk4dGPj6ZtDYsERgAAAAD7mMnCo0WllN9sd7HW+qEG+gEAAACgi0wWHvUkmR/DsQEAAAD2WZOFRw/WWv9g2joBAAAAoOu0JrlmxxEAAADAPm6y8OiF09YFAAAAAF2pbXhUa31sOhsBAAAAoPtMtvMIAAAAgH2c8AgAAACAtoRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0JbwCAAAAIC2hEcAAAAAtCU8AgAAAKAt4REAAAAAbQmPAAAAAGhLeAQAAABAW8IjAAAAANoSHgEAAADQlvAIAAAAgLaERwAAAAC0JTwCAAAAoC3hEQAAAABtCY8AAAAAaEt4BAAAAEBbwiMAAAAA2hIeAQAAANCW8AgAAACAtoRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0JbwCAAAAIC2hEcAAAAAtCU8AgAAAKAt4REAAAAAbQmPAAAAAGhLeAQAAABAW8IjAAAAANoSHgEAAADQVmPhUSnl06WUVaWUGydZ87xSynWllJtKKZc21QsAAAAAu6fJnUd/neTcdhdLKfsn+ViSl9ZaT0ry6gZ7AQAAAGA3NBYe1VovS/LYJEtel+TiWuu9W9evaqoXAAAAAHbPTM48Oj7JAaWUS0opV5dS3jSDvQAAAACwA70z/LnPSPLCJHOSfL+UckWt9bbtF5ZSLkhyQZIsWbJkWpsEAAAA2JfN5M6jlUm+VmvdWGt9JMllSU7d0cJa64W11mW11mWLFi2a1iYBAAAA9mUzGR59OcnZpZTeUsrcJGcmWTGD/QAAAACwncaOrZVSPp/keUkOLqWsTPJfk/QlSa31E7XWFaWUryW5PslEkr+qtd7YVD8AAAAA7LrGwqNa62unsOaDST7YVA8AAAAA7JmZPLYGAAAAQJcTHgEAAADQlvAIAAAAgLaERwAAAAC0JTwCAAAAoC3hEQAAAABtCY8AAAAAaEt4BAAAAEBbwiMAAAAA2hIeAQAAANCW8AgAAACAtoRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0JbwCAAAAIC2hEcAAAAAtCU8AgAAAKAt4REAAAAAbQmPAAAAAGhLeAQAAABAW8IjAAAAANoSHgEAAADQlvAIAAAAgLaERwAAAAC0JTwCAAAAoC3hEQAAAABtCY8AAAAAaEt4BAAAAEBbwiMAAAAA2hIeAQAAANCW8AgAAACAtoRHAAAAALQlPAIAAACgLeERAAAAAG0JjwAAAABoS3gEAAAAQFvCIwAAAADaEh4BAAAA0JbwCAAAAIC2hEcAAAAAtCU8AgAAAKAt4REAAAAAbQmPAP5ve/cerHdd3wn8/TmX5CQHAiEB5J4g3rJIUKKCuHijHVxXWV07ar3u6qjdWrvajtWuu7rjjNOOu620apVSS61WtIqUddtCRau1IIJcFAQLcglBIFzCzVzO7bt/5GRkaZ5cn+c8eY6v18yZPOf5/c4n75P8JpO88/19fwAAAHSkPAIAAACgI+URAAAAAB0pjwAAAADoSHkEAAAAQEfKIwAAAAA6Gul3AAAAAIB93boNG3PB1Xfm0p/cnx/f/Ug2T01ndHgoK5aN5+RjD8rLVx+RVYcv6XfMnlAeAQAAAHRwxwMb8/7zf5grbnsgM61lcro95uh0rtn4YH6w7sGce+ltWbl8PL/3yhOy+qgD+5a3F9y2BgAAALAdX7xibX75D7+dy265P1umZh5XHP3cTEs2T87khrseyas/fVk+8rc3ZGZm++cOIuURAAAAwON88h9vzocuvD6bJqczvRtF0OapmfzlZbfnXeddPW8KJOURAAAAwGNcfP3d+eNLbsqmyZk9+vpNk9O55Ib1OeuSm7qcrD+URwAAAACzNvxsIr/919fucXG0zabJ6Xz6Wz/JDXc93KVk/aM8AgAAAJj1qW/9JJun9q442mbL1Ez+5/+5viuz+kl5BAAAAJBkYmomn798bSa6VB61JFevfTB3PLCxK/P6RXkEAAAAkOSqtRt6MvcbN67vydy50rPyqKo+U1Xrq+q6nZz3rKqarqpX9SoLAAAAwM78cN1DXVt1tM2WqZl879b7uzpzrvVy5dG5Sc7Y0QlVNZzk95Nc1MMcAAAAADt1490PZ2K6u+VRkty0/tGuz5xLPSuPWmvfTvLATk77jSRfSTLY67cAAACAgTc103ozd7o3c+dK3/Y8qqojkrwiyaf6lQEAAABgm6WLF/Rk7v5jIz2ZO1f6uWH2x5L8TmttemcnVtXbqurKqrry3nvvnYNoAAAAwC+aE448IOMLhrs6syo56ZiDujpzrvWz+lqT5LyqSpLlSf5dVU211i54/ImttbOTnJ0ka9asGey1XgAAAMA+6RlHL810627tML5gOM9eqTzaI621ldteV9W5Sb62veIIAAAAYC6sXD6elcvHc8Ndj3RxauWFTz24i/PmXs9uW6uqLyS5LMlTqmpdVb2lqt5RVe/o1c8JAAAAsDfe9aInZXGXbl0bGx3KG085JgtHunsr3Fzr2cqj1tprd+PcN/cqBwAAAMCuOuP4J+Sz3709V9z6wF4/fW3p4gV514uf1KVk/dPPDbMBAAAA9ilVlbNefWKWjI1m6zbNe2bR6HA+9fqTMjY62KuOEuURAAAAwP/nkCVj+cp/eW6WLl6Q0eHda5CGKlm0YDh/9uY1WX3UgT1KOLf6+bS1X1jrH9mc63/6cB7aOJmhocphB4xl1WFLMr7QbwcAAADsC1YuH8/X3/P8/M6Xr813br4/myand/o1ixcMZ8Wy8Xzidc/MyuXjc5Bybmgr5sg9D2/O5757ez5/+do8unkqC0eGMtNaUkmlsnlyOiuWj+ftpx2bl60+fF4sawMAAIBBdtD4gvzpm56Vf775vnzymzfne7c9kIUjw9kyNZ2p6ZbhocrY6HAmpmfyxIPH82svOC4vffphGR7ai/vd9kHV2t5t/jTX1qxZ06688sp+x9hl0zMtf/Ktm/Pxb9yc1pItUzM7PH/xguGMjQznY685Mac9ebAf5QcAAADzyUMbJ3PdTx/KDXc9nI0T01kwMpTjDt4vTz/ygBy6ZKzf8fZKVX2/tbZmu8eUR73z4MaJvO6cy3PLfT/LpomdL297rLHRobzmWUfngy9bldqbHboAAAAAdmJH5ZHb1nrk4c2TeeUnL80dGzZmcnr3C7rNkzP54hV3ZMvUTD7yiuMVSAAAAEBfeNpaj/z2l67Nuj0sjrbZNDmdC66+MxdcfWcXkwEAAADsOuVRD1x8/d35p5vuy8ReFEfbbJqczgcuuC7rH9nchWQAAAAAu0d51GWttXz4//5olx7ht6smpmfyp9++pWvzAAAAAHaV8qjLvn/7htz/6ERXZ05Ot/zV9+7IlqnuFVIAAAAAu0J51GUXXX/3bj9ZbVdUkmvWPtj1uQAAAAA7ojzqsstvfSB7v9PRvzYxPZMf3vlQDyYDAAAAdKY86rK192/sydyJqZn86K6HezIbAAAAoBPlUZdNzfRi3dFWWyZnejYbAAAAYHuUR122cKR3v6T7jY30bDYAAADA9iiPuuy4Q/brydxFo8M54cgDejIbAAAAoBPlUZedfOxBGRmqrs8dquTpRyiPAAAAgLmlPOqyl60+oifl0eKFIzn+cOURAAAAMLeUR1123CH75SmH7d/VmWOjQ3nr81ZmqAelFAAAAMCOKI964MNnHp+x0e790i4ZG80bT1nRtXkAAAAAu0p51AMnHHlg3nTKiizqQoE0NjqUj//qM7NowXAXkgEAAADsHuVRj7z3jKfmlCcu36sVSGOjQ/nAS1fl2SsP6mIyAAAAgF2nPOqR4aHK2W84KS874fAsGt29VUPDVVk0OpyPvOLpef3Jx/QoIQAAAMDOKY96aGR4KB/9ldX59BtOyvL9FmR8J7eeDQ9VFo0OZfVRB+Tid5+WVz7zyDlKCgAAALB9I/0O8IvgtCcfnMt/9/R888b1OffS23LtugczMTWTkeGtT0/bMjmTZfstyPOOW563PO/YrDp8SZ8TAwAAAGylPJojw0OV01cdmtNXHZokWf/I5jy0cTJDQ5VDl4xlv4V+KwAAAIB9j8aiTw7ZfyyH7D/W7xgAAAAAO2TPIwAAAAA6Uh4BAAAA0JHyCAAAAICOlEcAAAAAdKQ8AgAAAKAj5REAAAAAHSmPAAAAAOhIeQQAAABAR8ojAAAAADpSHgEAAADQkfIIAAAAgI6URwAAAAB0pDwCAAAAoCPlEQAAAAAdKY8AAAAA6Eh5BAAAAEBHyiMAAAAAOlIeAQAAANCR8ggAAACAjpRHAAAAAHSkPAIAAACgI+URAAAAAB0pjwAAAADoSHkEAAAAQEfKIwAAAAA6Uh4BAAAA0JHyCAAAAICOlEcAAAAAdKQ8AgAAAKAj5REAAAAAHSmPAAAAAOhIeQQAAABAR8ojAAAAADrqWXlUVZ+pqvVVdV2H46+rqh/MflxaVat7lQUAAACAPdPLlUfnJjljB8dvTfL81toJST6c5OweZgEAAABgD4z0anBr7dtVtWIHxy99zKffTXJkr7IAAAAAsGd6Vh7tprck+bt+h2D+mpyeyaObp1KV7D82muGh6nckAAAAGAh9L4+q6oXZWh49bwfnvC3J25Lk6KOPnqNkDLrr7nwof3nZ7fnurfdn3YZNWwujlrS0rFg2nuc/+eC84ZRjcsyy8X5HBQAAgH1WtdZ6N3zrbWtfa60d3+H4CUm+muQlrbV/2ZWZa9asaVdeeWXXMjL/3HDXw3nPl67JbfdtzJap6cx0uMRHhypDQ5U1K5bmo69ancMPXDS3QQEAAGAfUVXfb62t2d6xXm6YvUNVdXSS85O8YVeLI9iR1lr+6JKb8opP/nNuvOuRbJrsXBwlyeRMy5apmXz3lgfy4j/4Vr561bq5CwsAAAADome3rVXVF5K8IMnyqlqX5INJRpOktfapJP8jybIkn6yqJJnq1HDBzrTW8oELrsv5V92ZzZMzu/W10zMtmyam8/6v/jAPbZrMm09d2aOUAAAAMHh6+bS11+7k+FuTvLVXPz+/WM75zq05/6o7s2lyeo9nbJ6cye///Y05Zvl4XviUQ7qYDgAAAAZX325bg2655d5H878v/vFeFUfbbJqcybvPuyYPbZrsQjIAAAAYfMojBt5/v+C6TEzt3q1qO7JxYipnfd02XAAAAJAojxhw6zZszJW3b9jhxti7a2K65bwr7sjmLqxkAgAAgEGnPGKgffXqOzPTutgczRqq5B9/vL7rcwEAAGDQKI8YaJfefH8mp7tfHm2cmM73b9/Q9bkAAAAwaJRHDLQf3/NIT+bOtCiPAAAAIMojBtzGiamezfbENQAAAFAeMeCGqno2e3iod7MBAABgUCiPGGiHLlnYs9nHLBvv2WwAAAAYFMojBtozjlrak7kLRobynJUH9WQ2AAAADBLlEQPtxU87NOMLh7s+d6iSU49b3vW5AAAAMGiURwy0X1p1aE/2PVqxbDxPO2xJ1+cCAADAoFEeMdAWjAzlHc9/YhaNdu9SXjQ6nN/65ad0bR4AAAAMMuURA+/tpx2bww9clG6sPxodrpx63LL80qpDuzANAAAABp/yiIE3MjyUs9+4JuMLR/ZqzvBQZdn4wnz0Vau7lAwAAAAGn/KIeeGJB++X8952cvYfG8nI0O6vQVo4MpRD9l+Yr/76c7N0fEEPEgIAAMBgUh4xbxx/xAH5h3c/PycdszSLF+z6E9gWjQ7nJcc/IRe9+7QcdsCiHiYEAACAwbN39/nAPuYJB4zlvLednIuuvyef+OZN+Zd7Hs3IUGXjxHTa7DlDlSxeMJLJ6Zk8e+VBeecLj8tzjl3W19wAAACwr1IeMe9UVc44/gk54/gnZO39G3P1HRty1doNWf/wllQlRy1dnBOPOjAnHbM0hywZ63dcAAAA2Kcpj5jXjl62OEcvW5wzTzyi31EAAABgINnzCAAAAICOlEcAAAAAdKQ8AgAAAKAj5REAAAAAHSmPAAAAAOhIeQQAAABAR8ojAAAAADpSHgEAAADQkfIIAAAAgI6URwAAAAB0pDwCAAAAoCPlEQAAAAAdKY8AAAAA6Eh5BAAAAEBHyiMAAAAAOlIeAQAAANCR8ggAAACAjpRHAAAAAHSkPAIAAACgo2qt9TvDbqmqe5Pc3u8cXbQ8yX39DgE94vpmPnN9cS0jlgAABfFJREFUM5+5vpnPXN/Md65x9tQxrbWDt3dg4Mqj+aaqrmytrel3DugF1zfzmeub+cz1zXzm+ma+c43TC25bAwAAAKAj5REAAAAAHSmP+u/sfgeAHnJ9M5+5vpnPXN/MZ65v5jvXOF1nzyMAAAAAOrLyCAAAAICOlEd9UlVnVNWPq+rmqnpfv/NAN1XVUVX1zaq6oaqur6rf7Hcm6KaqGq6qq6vqa/3OAt1WVQdW1Zer6sbZP8dP6Xcm6Jaqevfs302uq6ovVNVYvzPBnqqqz1TV+qq67jHvHVRV/1BVN83+uLSfGZk/lEd9UFXDST6R5CVJViV5bVWt6m8q6KqpJL/VWntakpOT/LprnHnmN5Pc0O8Q0CNnJfn71tpTk6yOa515oqqOSPKuJGtaa8cnGU7ymv6mgr1ybpIzHvfe+5Jc0lp7UpJLZj+HvaY86o9nJ7m5tXZLa20iyXlJzuxzJuia1tpdrbWrZl8/kq3/8Diiv6mgO6rqyCQvTXJOv7NAt1XVkiSnJfmzJGmtTbTWHuxvKuiqkSSLqmokyeIkP+1zHthjrbVvJ3ngcW+fmeQvZl//RZL/MKehmLeUR/1xRJI7HvP5uviHNfNUVa1I8owkl/c3CXTNx5K8N8lMv4NADxyb5N4kfz57a+Y5VTXe71DQDa21O5P8ryRrk9yV5KHW2sX9TQVdd2hr7a5k63/oJjmkz3mYJ5RH/VHbec9j75h3qmq/JF9J8l9baw/3Ow/srar690nWt9a+3+8s0CMjSZ6Z5E9aa89I8rO45YF5YnbvlzOTrExyeJLxqnp9f1MBDAblUX+sS3LUYz4/MpbMMs9U1Wi2Fkefb62d3+880CWnJnl5Vd2Wrbccv6iqPtffSNBV65Ksa61tWy365Wwtk2A+OD3Jra21e1trk0nOT/LcPmeCbrunqg5Lktkf1/c5D/OE8qg/rkjypKpaWVULsnWjvgv7nAm6pqoqW/fLuKG19gf9zgPd0lp7f2vtyNbaimz9s/sbrTX/a8280Vq7O8kdVfWU2bdenORHfYwE3bQ2yclVtXj27yovjg3hmX8uTPKm2ddvSvI3fczCPDLS7wC/iFprU1X1ziQXZetTHj7TWru+z7Ggm05N8oYkP6yqa2bf+93W2t/2MRMAu+Y3knx+9j+4bknyn/qcB7qitXZ5VX05yVXZ+mTYq5Oc3d9UsOeq6gtJXpBkeVWtS/LBJL+X5EtV9ZZsLUx/pX8JmU+qNVvtAAAAALB9blsDAAAAoCPlEQAAAAAdKY8AAAAA6Eh5BAAAAEBHyiMAAAAAOlIeAQDsQFU92u8MAAD9pDwCANgHVNVIvzMAAGyP8ggAYDdV1cuq6vKqurqqvl5Vh1bVUFXdVFUHz54zVFU3V9Xyqjq4qr5SVVfMfpw6e86Hqursqro4yWer6t9U1feq6pqq+kFVPamv3ygAQJRHAAB74jtJTm6tPSPJeUne21qbSfK5JK+bPef0JNe21u5LclaSP2ytPSvJf0xyzmNmnZTkzNbaryZ5R5KzWmsnJlmTZN2cfDcAADtgeTQAwO47MskXq+qwJAuS3Dr7/meS/E2SjyX5z0n+fPb905OsqqptX7+kqvaffX1ha23T7OvLkvy3qjoyyfmttZt6+20AAOyclUcAALvvj5N8vLX29CRvTzKWJK21O5LcU1UvSvKcJH83e/5QklNaayfOfhzRWntk9tjPtg1trf1Vkpcn2ZTkotk5AAB9pTwCANh9ByS5c/b1mx537JxsvX3tS6216dn3Lk7yzm0nVNWJ2xtaVccmuaW19kdJLkxyQjdDAwDsCeURAMCOLa6qdY/5eE+SDyX566r6pyT3Pe78C5Psl5/fspYk70qyZnYT7B9l695G2/PqJNdV1TVJnprks938RgAA9kS11vqdAQBg3qiqNdm6Ofa/7XcWAIBusGE2AECXVNX7kvxafv7ENQCAgWflEQAAAAAd2fMIAAAAgI6URwAAAAB0pDwCAAAAoCPlEQAAAAAdKY8AAAAA6Eh5BAAAAEBH/w+4QyuXVVdcKwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "\n",
    "# replacing 0s with 1s. np.log(1) = 0 and np.log(0) = -inf\n",
    "layer_attrs_end_pdf[layer_attrs_end_pdf == 0] = 1\n",
    "layer_attrs_end_pdf_log = np.log2(layer_attrs_end_pdf)\n",
    "\n",
    "# size: #layers\n",
    "entropies= -(layer_attrs_end_pdf * layer_attrs_end_pdf_log).sum(0)\n",
    "\n",
    "plt.scatter(np.arange(12), attr_sum, s=entropies * 100)\n",
    "plt.xlabel('Layers')\n",
    "plt.ylabel('Total Attribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Part 2 of this tutorial we will go deeper into attention layers, heads and compare the attributions with the attention weight matrices, study and discuss related statistics."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
